Predictive analytics in retail: a guide

What is predictive analytics in the retail industry?

Predictive analytics in the retail industry is when vendors take existing and real-time data to spot trends in their customers’ buying behavior, market trends, logistics, inventories and competitor products. 

The hint is in the name: predictive. Instead of looking back at what customers have already done, predictive analytics allows vendors to make data-backed decisions about the future, based on current and historical consumer behavior and market trends. 

But exactly how does predictive analytics help retailers?

Think about what your selling process looks like right now and all of the information you gather; from learning about what items are selling fast to how many products you’ve got left in stock. Each of these data points on their own may not tell you much, but combining them helps vendors see shifts in buying trends that may otherwise slip under your radar. 

That is the power of predictive analytics. It can keep you ahead of the curve and give your customers what they want before they even know.

Predictive analytics helps with the behind-the-scenes stuff as well.

From risk analysis to forecasting how much stock you should order, the predictive analysis of data can help with every part of your supply chain, from the warehouse all the way through to the checkout. It can also enable higher levels of forecasting accuracy, using metrics like customer lifetime value and churn analysis to ensure that you’ll have a clearer, more joined-up picture of just how much money your brand is (or will be) making. Combine predictive analytics with a generous helping of AI-powered Decision Intelligence, and you can be sure that you’re making the right decision, all the time, to drive the optimal outcomes for your brand. 

However, there are two distinct ways in which predictive analytics can be used: to model and to maintain.

Predictive modeling in retail

Predictive modeling is when analytics and data are used to model buyer behavior. In retail, this can help brands predict what products will be in high demand, or if customers will be likely to want to purchase add-ons with particular items. It’ll also make day-to-day tasks like price setting and discounting easier by looking at factors like consumer confidence and what your competitors are doing.

Predictive maintenance in retail

Predictive maintenance differs from modeling, as it helps retailers get a better insight into how their operations, inventories and logistics are running. 

For example, predictive maintenance can take data about a company’s logistics or inventories and analyze when stock is running low, or warehouse space is running out. Instead of dealing with an issue after it’s happened, predictive maintenance gives you the heads up, so you have time to solve inventory or logistical problems before they happen.

How is predictive analytics changing the retail industry?

More accurate forecasting

When it comes to keeping your retail business running smoothly, forecasting is a critical capability. 

Imagine if your company could predict how much stock it will need over the holiday period and what products are the most likely to be your best sellers. Sounds like a retail dream, right? Predictive analytics brings your company one step closer to doing just that. 

Predictive analytics can collect and leverage data from both online and bricks-and-mortar stores to help predict future sales outcomes and maintain the perfect amount of trimmed-down inventory, so that you’re not paying thousands of dollars to store products that you’re not selling.

It can also use historical sales data to calculate which of your products are the most profitable, based on margins and how many units you’re selling.

Better customer personalization

Do you still send blanket marketing campaigns to your customers, serving them the same ‘special’ offer no matter who they are or how they shop?

Well, they want more. In fact, 36% of consumers now say that they want retailers to offer better personalized shopping experiences in order to get them through the door or browsing their website.

AI-powered predictive analytics gathers data so your brand can do just that. The average purchase will give your company a treasure trove of information about a customer, from where they live to their unique style preferences and even their likely budget. 

Instead of this data just sitting in a spreadsheet, predictive analytics uses it to uncover patterns in how your customers behave. Use it to figure out what social media channels customers are scrolling on (so you can personalize your target marketing) or learn if they prefer buying big-ticket items rather than lots of small purchases. Predictive analytics can leverage this data-backed evidence to help you make decisions like:

• Which segment is in-market to buy for each product, and is there a suitable lookalike audience to target?
• When is the best time to send the message to target them at the perfect point in their purchase journey?
• Which products should I recommend to each customer in order to improve their onsite experience and increase sales?

With predictive analytics you can build a more customized marketing campaign that speaks to specific shoppers instead of using blanket messaging on the back of these insights.

Precise customer segmentation

Thanks to technology like predictive analytics, retail brands can now track a customer’s entire buying journey; from the moment they click on an ad until the time a package arrives on their doorstep. 

Predictive analytics capabilities allow retailers to take all this data, segment customers into relevant groups and learn more about them. If a customer buys something from you, predictive analytics can then scan their buying history and use that data to recommend other products they may like and upsell items to boost revenue.

Smarter pricing data

Pricing is always a dilemma for vendors; charge too little, and you’ll be out of pocket. Charge too much, and your customers will shop elsewhere.

Predictive analytics takes pricing data to make suggestions about how much you should be charging for products. For example, predictive analytics can study your product pricing data against other factors like market fluctuations, consumer demand and competitor pricing to give you data-based suggestions on how much you should be charging. 

Now that you know the basics of how predictive analytics is used in retail, let’s get specific and take things to the next level ?

Taking predictive analytics one step further with Decision Intelligence

Decision Intelligence takes the overall concept of predictive analytics but turns it up to 11. It’s the commercial application of AI to drive profit and growth, and involves leveraging a retailer’s data from across the entire business, enhancing it with an AI that makes predictions and categorizations over this data.

In short, it enables businesses in the retail sector to stop hoping they’re making the right decisions around stock, supply chain or marketing – and start knowing that they have, with the data to back it up!

Before working with Peak and accessing our Decision Intelligence platform, Footasylum struggled with delivering a personalized experience to its customers as its product offering was so vast.

The brand now uses Decision Intelligence to give hyper-personalized experiences to every customer, which has boosted customer loyalty and growth. 

How? Footasylum took its years (and years) worth of past customer data to learn more about the people they were selling to and give them a better experience. 

The brand enlisted Peak’s help to get a predictive view of its customers. Footasylum now utilizes AI and machine learning-powered algorithms to get insights based on the historical customer behavior, which gives them a realistic look at each customer’s lifetime value and churn risk, as well as their brand and style preferences. 

Peak has also helped Footasylum predict which customers are likely to be in-market using lookalike audiences that are targeted with different products based on regionality, persona and much more.

How is predictive analytics used in retail? What else can Decision Intelligence do?

Predictive analytics and Decision Intelligence can help you make sense of customer behavior

Your customers are shopping from everywhere – in-store, on their mobiles, on their laptops… basically anywhere there’s an internet connection, there’s a way for people to buy something from you. 

And each time a customer interacts with your brand, it’s a chance to learn more about their age, gender, location and even how often they visit your website or make a purchase

Imagine if you had a customer in Los Angeles who has purchased some NBA products from your site in the past. Thanks to technologies like predictive analytics and by taking a Decision Intelligence-powered approach to your datasets, we can also see that they usually buy products at the end of the month (possibly when they get paid) and nearer to the playoff season (around May – July). There are also patterns in the items they buy: jerseys and hats with a vintage feel. 

Without predictive analytics, AI or machine learning, it would take a human hours to connect these dots and realize the best way to market to this customer: offering vintage apparel at the end of the month when the NBA season is heading into the playoffs. 

Making sense of all your data points allows you to target customers better and market products to them that they actually want. 

Decision Intelligence and predictive analytics can personalize every shopper’s journey

No customer is the same – so why do so many buying journeys look the same?

Think about every customer that buys something from you, how different their needs are and how many touchpoints they pass through. They may be buying from your website, your app or in-store. Each of these touchpoints gathers data about who the customer is, what they’re buying, if they’re a frequent customer and much, much more. 

Forward-thinking, data-driven retailers can then take this data and personalize every single touchpoint, from preferred payment methods based on past history to driving upsells that are tied to the customer’s styles and budget. 

Decision Intelligence and predictive data analysis can solve difficult supply chain and inventory problems

Reducing overstock can lower overall inventory costs by as much as 10%. And it’s easy to see why – in the US alone, retailers are sitting on approximately $1.36 of stock for every $1 they make in sales. 

Retailers are literally sitting on money because their supply and inventory chains aren’t optimized. 

But with the help of predictive analytics and Decision Intelligence, you can change this. Instead of buying huge amounts of particular stock because you think it might sell over the next quarter, Decision Intelligence uses data to optimize inventory and adjust to demand fluctuations. It can give insights on when to:

• Adjust your safety stock (by how much and when to do it)
• Restock your inventory (when you need to do it and how much you need)
• Move your inventory to another location

Retail brands can use Peak’s Inventory Optimization solution to make sure they’ve got the right inventory on hand and that they’re adjusted according to market conditions and the store’s business objectives. 

Not only does it use AI and predictive analysis to make demand and supply predictions, but it also helps to improve SKU profitability, reduce out-of-stock incidents, improve customer satisfaction, decrease inventory costs and increase revenues.

It can help with logistics, too. Sure, you don’t need a piece of software to tell you not to schedule meetings on Monday morning or Friday afternoons. But the right predictive analytics tools can uncover the best times for your store to receive shipments and send out orders to customers, so they reach them in time.

Predictive analytics and Decision Intelligence in retail: wrapping up

Predictive analytics in retail is much more than just a buzzword – it’s a game-changer for forward-thinking retailers who want to optimize every aspect of their company. However, those brands who are winning in today’s competitive retail landscape are taking their predictive analytics projects and digital transformation journeys to the next level with Decision Intelligence. 

From improving logistics and inventory chains to personalizing customer journeys, Decision Intelligence finally makes sense of all the data retailers have been collecting for decades.

And with this insight, retailers like you can do what you do best – sell products – in a smarter way that delights your customers and boosts your bottom line!  

Here at Peak, we help retailers like you get more value from your data and empower you to reap the benefits of predictive analytics. However, we place our focus on ensuring you can tie it back to real-life commercial outcomes with Decision Intelligence. 

Decision Intelligence enables retailers to make superhuman decisions every single time. We’ve worked alongside retailers PrettyLittleThing, Footasylum, and Essentra Components use predictive analytics to get the most out of their data — read more about their success here.